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README.Rmd
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README.Rmd
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---
output: github_document
---
<!-- README.md is generated from README.Rmd. Please edit that file -->
```{r, echo = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
echo = TRUE,
message = FALSE,
error = FALSE,
comment = "#>",
fig.path = "man/figures/"
)
```
# FacileData
<!-- badges: start -->
[](https://github.com/facilebio/FacileData/actions)
[](http://www.repostatus.org/#active)
[](https://www.tidyverse.org/lifecycle/#maturing)
[](https://codecov.io/gh/facilebio/FacileData)
<!-- badges: end -->
The `FacileData` package was written to facilitate easier analysis of large,
multi-assay high-throughput genomics datasets. To this end, the `FacileData`
package provides two things:
1. A *FacileData Access API* that defines a fluent interface over multi-assay
genomics datasets that fits into the [tidyverse][tidyverse]. This enables
analysts to more naturally query and retrieve data for general exploratory
data analysis; and
2. A reference implementation of a datastore that implements the
*FacileData Access API* called a *FacileDataSet*. The `FacileDataSet`
provides efficient storage and retrieval of arbitrarily large high-throughput
genomics datasets. For example, a single `FacileDataSet` can be used to store
*all* of the RNA-seq, microarray, RPPA, etc. data from the
[The Cancer Genome Atlas][tcga]. This singular `FacileDataSet` allows
analysts easy access to arbitrary subsets of these data without having to
load all of it into memory.
[tcga]: https://cancergenome.nih.gov/
[tidyverse]: https://www.tidyverse.org/
# Installation
The FacileData suite of packages is only available from github from now. You
will want to install three `FacileData*` packages to appreciate the its utility:
```{r gh-installation, eval = FALSE}
# install.packages("devtools")
devtools::install_github("facilebio/FacileData")
```
# Example Usage
As a teaser, we'll show how to plot HER2 copy number vs expression across the
TCGA bladder and breast indications ("BLCA" and "BRCA") using a `FacileDataSet`.
```{r her2-cnv-expression, eval = FALSE}
library(ggplot2)
library(FacileData)
library(FacileTCGADataSet)
tcga <- FacileTCGADataSet()
features <- filter_features(tcga, name == "ERBB2")
fdat <- tcga |>
filter_samples(indication %in% c("BLCA", "BRCA")) |>
with_assay_data(features, assay_name = "rnaseq", normalized = TRUE) |>
with_assay_data(features, assay_name = "cnv_score") |>
with_sample_covariates(c("indication", "sex"))
ggplot(fdat, aes(cnv_score_ERBB2, ERBB2, color = sex)) +
geom_point() +
facet_wrap(~ indication)
```
<img src="man/figures/her2_cnv_vs_expression.png" width="66%" />
Let's compare how you might do the same using data stored in a
`SummarizedExperiment` named `se.tcga` that stores RNA-seq (raw and normalized)
and copy number data.
```{r example-sumexp, eval = FALSE}
# load / get `se.all` from somewhere
fidx <- which(mcols(se.all)$name == "ERBB2")
se <- se.all[, se.all$indication %in% c("BLCA", "BRCA")]
sdat <- data.frame(
ERBB2 = assay(se, "rnaseq_norm")[fidx,],
cnv_score_ERBB2 = assay(se, "cnv_score")[fidx,],
sex = se$sex,
indication = se$indication)
ggplot(fdat, aes(cnv_score_ERBB2, ERBB2, color=sex)) +
geom_point() +
facet_wrap(~ indication)
```
TODO: Show same analysis using MultiAssayEperiment